





'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


a__			:					int       						=						{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

a__			:					Dict       						=						logging.get_logger(__name__)






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								="maskformer"
       _lowerCamelCase								={"hidden_size": "mask_feature_size"}
       _lowerCamelCase								=["resnet", "swin"]
       _lowerCamelCase								=["detr"]

       def __init__( self					:				Tuple       ,     a__					:				int = 256       ,     a__					:				int = 256       ,     a__					:				float = 0.1       ,     a__					:				bool = False       ,     a__					:				Optional[Dict] = None       ,     a__					:				Optional[Dict] = None       ,     a__					:				float = 0.02       ,     a__					:				float = 1.0       ,     a__					:				float = 1.0       ,     a__					:				float = 1.0       ,     a__					:				float = 20.0       ,     a__					:				Optional[bool] = None       ,     **a__					:				List[Any]       ,     ):
              if backbone_config is None:
                     # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                     UpperCAmelCase  =						SwinConfig(
                         image_size=384       ,     in_channels=3       ,     patch_size=4       ,     embed_dim=128       ,     depths=[2, 2, 18, 2]       ,     num_heads=[4, 8, 16, 32]       ,     window_size=12       ,     drop_path_rate=0.3       ,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       ,     )

              if isinstance(a__       ,     a__						):
                     UpperCAmelCase  =						backbone_config.pop('''model_type'''						)
                     UpperCAmelCase  =						CONFIG_MAPPING[backbone_model_type]
                     UpperCAmelCase  =						config_class.from_dict(a__						)

              # verify that the backbone is supported
              if backbone_config.model_type not in self.backbones_supported:
                     logger.warning_once(
                         f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
                         f"Supported model types: {','.join(self.backbones_supported						)}"						)

              if decoder_config is None:
                     # fall back to https://huggingface.co/facebook/detr-resnet-50
                     UpperCAmelCase  =						DetrConfig()
              else:
                     # verify that the decoder is supported
                     UpperCAmelCase  =						(
                         decoder_config.pop('''model_type'''						) if isinstance(a__       ,     a__						) else decoder_config.model_type
                     )
                     if decoder_type not in self.decoders_supported:
                            raise ValueError(
                                f"Transformer Decoder {decoder_type} not supported, please use one of"
                                f" {','.join(self.decoders_supported						)}"						)
                     if isinstance(a__       ,     a__						):
                            UpperCAmelCase  =						CONFIG_MAPPING[decoder_type]
                            UpperCAmelCase  =						config_class.from_dict(a__						)

              UpperCAmelCase  =						backbone_config
              UpperCAmelCase  =						decoder_config
              # main feature dimension for the model
              UpperCAmelCase  =						fpn_feature_size
              UpperCAmelCase  =						mask_feature_size
              # initializer
              UpperCAmelCase  =						init_std
              UpperCAmelCase  =						init_xavier_std
              # Hungarian matcher && loss
              UpperCAmelCase  =						cross_entropy_weight
              UpperCAmelCase  =						dice_weight
              UpperCAmelCase  =						mask_weight
              UpperCAmelCase  =						use_auxiliary_loss
              UpperCAmelCase  =						no_object_weight
              UpperCAmelCase  =						output_auxiliary_logits

              UpperCAmelCase  =						self.decoder_config.encoder_attention_heads
              UpperCAmelCase  =						self.decoder_config.num_hidden_layers
              super().__init__(**a__						)

       @classmethod
       def 			__snake_case      ( cls					:				Tuple       ,     a__					:				PretrainedConfig       ,     a__					:				PretrainedConfig       ,     **a__					:				List[str]						):
              return cls(
                  backbone_config=a__       ,     decoder_config=a__       ,     **a__       ,     )



       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						copy.deepcopy(self.__dict__						)
              UpperCAmelCase  =						self.backbone_config.to_dict()
              UpperCAmelCase  =						self.decoder_config.to_dict()
              UpperCAmelCase  =						self.__class__.model_type
              return output








'''simple docstring'''







import argparse
import copy







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   with open(UpperCAmelCase						) as f:
      for line in f:
         if line.split()[0] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[1], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[0]].append(
                [line.split()[1], line.split()[2]]						)
         if line.split()[1] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[0], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[1]].append(
                [line.split()[0], line.split()[2]]						)

   return dict_of_neighbours







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      A__       =     f.read(1						)
   A__       =     start_node

   A__       =     []

   A__       =     start_node

   A__       =     0
   while visiting not in first_solution:
      A__       =     10000
      for k in dict_of_neighbours[visiting]:
         if int(k[1]						) < int(UpperCAmelCase						) and k[0] not in first_solution:
            A__       =     k[1]
            A__       =     k[0]

      first_solution.append(UpperCAmelCase						)
      A__       =     distance_of_first_solution + int(UpperCAmelCase						)
      A__       =     best_node

   first_solution.append(UpperCAmelCase						)

   A__       =     0
   for k in dict_of_neighbours[first_solution[-2]]:
      if k[0] == start_node:
         break
      position += 1

   A__       =     (
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 10000
   )
   return first_solution, distance_of_first_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for n in solution[1:-1]:
      A__       =     solution.index(UpperCAmelCase						)
      for kn in solution[1:-1]:
         A__       =     solution.index(UpperCAmelCase						)
         if n == kn:
            continue

         A__       =     copy.deepcopy(UpperCAmelCase						)
         A__       =     kn
         A__       =     n

         A__       =     0

         for k in _tmp[:-1]:
            A__       =     _tmp[_tmp.index(UpperCAmelCase						) + 1]
            for i in dict_of_neighbours[k]:
               if i[0] == next_node:
                  A__       =     distance + int(i[1]						)
         _tmp.append(UpperCAmelCase						)

         if _tmp not in neighborhood_of_solution:
            neighborhood_of_solution.append(_tmp						)

   A__       =     len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda UpperCAmelCase						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     1
   A__       =     first_solution
   A__       =     []
   A__       =     distance_of_first_solution
   A__       =     solution

   while count <= iters:
      A__       =     find_neighborhood(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     0
      A__       =     neighborhood[index_of_best_solution]
      A__       =     len(UpperCAmelCase						) - 1

      A__       =     False
      while not found:
         A__       =     0
         while i < len(UpperCAmelCase						):
            if best_solution[i] != solution[i]:
               A__       =     best_solution[i]
               A__       =     solution[i]
               break
            A__       =     i + 1

         if [first_exchange_node, second_exchange_node] not in tabu_list and [
             second_exchange_node,
             first_exchange_node,
         ] not in tabu_list:
            tabu_list.append([first_exchange_node, second_exchange_node]						)
            A__       =     True
            A__       =     best_solution[:-1]
            A__       =     neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               A__       =     cost
               A__       =     solution
         else:
            A__       =     index_of_best_solution + 1
            A__       =     neighborhood[index_of_best_solution]

      if len(UpperCAmelCase						) >= size:
         tabu_list.pop(0						)

      A__       =     count + 1

   return best_solution_ever, best_cost







def 						_A      ( UpperCAmelCase=None						):






   '''simple docstring'''







   A__       =     generate_neighbours(args.File						)

   A__  ,			A__       =     generate_first_solution(
       args.File      ,UpperCAmelCase						)

   A__  ,			A__       =     tabu_search(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,args.Iterations      ,args.Size      ,)

   print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''Tabu Search''')
    parser.add_argument(
        '''-f''',
        '''--File''',
        type=str,
        help='''Path to the file containing the data''',
        required=True,
    )
    parser.add_argument(
        '''-i''',
        '''--Iterations''',
        type=int,
        help='''How many iterations the algorithm should perform''',
        required=True,
    )
    parser.add_argument(
        '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


